def _handle_slice(self, idx):
idx = np.index_exp[idx]
frames, dims = None, None
if isinstance(idx, (tuple, list)):
if len(idx) == 1:
frames, dims = idx[0], slice(None, None, None)
if len(idx) == 2:
frames, dims = idx[0], idx[1]
if len(idx) > 2:
raise IndexError(
"Slice was more than two-dimensional, not supported.")
cumsum = np.cumsum(self._source.trajectory_lengths())
if not isinstance(frames, (list, np.ndarray)):
frames = self._get_indices(frames, cumsum[-1])
dims = self._get_indices(dims, self._source.ndim)
nframes = len(frames)
ndims = len(dims)
data = np.empty((nframes, ndims), dtype=self._source.output_type())
from pyemma.coordinates.clustering import UniformTimeClustering
for i, x in enumerate(frames):
traj, idx = UniformTimeClustering._idx_to_traj_idx(x, cumsum)
data[i, :] = self._source.data[traj][idx, dims]
return data
def setUpClass(cls):
from pyemma.coordinates.clustering import KmeansClustering, RegularSpaceClustering, UniformTimeClustering
cls.dtraj_dir = tempfile.mkdtemp()
# generate Gaussian mixture
means = [
np.array([-3, 0]),
np.array([-1, 1]),
np.array([0, 0]),
np.array([1, -1]),
np.array([4, 2])
]
widths = [
np.array([0.3, 2]),
np.array([0.3, 2]),
np.array([0.3, 2]),
np.array([0.3, 2]),
np.array([0.3, 2])
]
# continuous trajectory
nsample = 1000
cls.T = len(means) * nsample
cls.X = np.zeros((cls.T, 2))
for i in range(len(means)):
cls.X[i * nsample:(i + 1) * nsample,
0] = widths[i][0] * np.random.randn() + means[i][0]
cls.X[i * nsample:(i + 1) * nsample,
1] = widths[i][1] * np.random.randn() + means[i][1]
# cluster in different ways
cls.km = KmeansClustering(n_clusters=100).estimate(cls.X)
cls.rs = RegularSpaceClustering(dmin=0.5).estimate(cls.X)
cls.rt = UniformTimeClustering(n_clusters=100).estimate(cls.X)
cls.cl = [cls.km, cls.rs, cls.rt]
return cls
def _get_itraj_random_accessible(self, itrajs, frames, dims):
itrajs = self._get_indices(itrajs, self._source.ntraj)
frames = self._get_indices(frames, sum(self._source.trajectory_lengths()[itrajs]))
dims = self._get_indices(dims, self._source.ndim)
nframes = len(frames)
ndims = len(dims)
if max(dims) > self._source.ndim:
raise IndexError("Data only has %s dimensions, wanted to slice by dimension %s."
% (self._source.ndim, max(dims)))
cumsum = np.cumsum(self._source.trajectory_lengths()[itrajs])
from pyemma.coordinates.clustering import UniformTimeClustering
ra = np.array([self._map_to_absolute_traj_idx(UniformTimeClustering._idx_to_traj_idx(x, cumsum), itrajs)
for x in frames])
indices = np.lexsort((ra[:, 1], ra[:, 0]))
ra = ra[indices]
data = np.empty((nframes, ndims), dtype=self._source.output_type())
curr = 0
for X in self._source.iterator(stride=ra, lag=0, chunk=0, return_trajindex=False):
L = len(X)
data[indices[curr:curr + L]] = X
curr += L
return data
def _handle_slice(self, idx):
idx = np.index_exp[idx]
frames, dims = None, None
if isinstance(idx, (tuple, list)):
if len(idx) == 1:
frames, dims = idx[0], slice(None, None, None)
if len(idx) == 2:
frames, dims = idx[0], idx[1]
if len(idx) > 2:
raise IndexError("Slice was more than two-dimensional, not supported.")
cumsum = np.cumsum(self._source.trajectory_lengths())
frames = self._get_indices(frames, cumsum[-1])
dims = self._get_indices(dims, self._source.ndim)
nframes = len(frames)
ndims = len(dims)
frames_order = frames.argsort().argsort()
frames_sorted = np.sort(frames)
from pyemma.coordinates.clustering import UniformTimeClustering
ra_stride = np.array([UniformTimeClustering._idx_to_traj_idx(x, cumsum) for x in frames_sorted])
data = np.empty((nframes, ndims), dtype=self._source.output_type())
offset = 0
for X in self._source.iterator(stride=ra_stride, lag=0, chunk=0, return_trajindex=False):
L = len(X)
data[offset:offset + L, :] = X[:, dims]
offset += L
return data[frames_order]
def _get_itraj_random_accessible(self, itrajs, frames, dims):
itrajs = self._get_indices(itrajs, self._source.ntraj)
frames = self._get_indices(frames, sum(self._source.trajectory_lengths()[itrajs]))
dims = self._get_indices(dims, self._source.ndim)
nframes = len(frames)
ndims = len(dims)
if max(dims) > self._source.ndim:
raise IndexError("Data only has %s dimensions, wanted to slice by dimension %s."
% (self._source.ndim, max(dims)))
cumsum = np.cumsum(self._source.trajectory_lengths()[itrajs])
data = np.empty((nframes, ndims), dtype=self._source.output_type())
from pyemma.coordinates.clustering import UniformTimeClustering
for i, x in enumerate(frames):
traj, idx = self._map_to_absolute_traj_idx(UniformTimeClustering._idx_to_traj_idx(x, cumsum), itrajs)
data[i, :] = self._source.data[traj][idx, dims]
return data
